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Artificial Immune Systems

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Jon Timmis – One of the best experts on this subject based on the ideXlab platform.

  • special issue on Artificial Immune Systems
    Swarm Intelligence, 2010
    Co-Authors: Jon Timmis, Paul S Andrews, Emma Hart

    Abstract:

    The field of Artificial Immune Systems (AIS) is a diverse area of research that bridges the disciplines of immunology and engineering. AIS algorithms are typically developed from the abstraction of Immune system theories, processes and agents, and they have been applied to a wide variety of engineering applications including computer security, fault tolerance, data mining and optimisation. More recently there has been a growing trend within AIS to facilitate closer interaction between the domains of immunology and engineering through the use of various mathematical and computational modelling approaches. These have included dynamical Systems analysis, agent-based modelling and cellular automata. The resulting models serve a dual purpose: to improve understanding of the biological domain, and to aid the development of more biologically inspired AIS for engineering problems. The field of swarm intelligence (SI) encompasses a wide range of scientific and engineering disciplines to explore and exploit the complex behaviours that arise from groupings of agents such as social insects or animals. Research in this field incorporates many decentralised and distributed Systems that exploit the collective behaviour that emerges from the interaction of individual agents with each other and their environment. This perspective affords a natural link between SI and AIS: many Immune algorithms operate in a very similar manner with populations of Immune agents exhibiting similar high-level collective behaviours; it has furthermore been suggested by several authors that the natural Immune

  • theoretical advances in Artificial Immune Systems
    Theoretical Computer Science, 2008
    Co-Authors: Jon Timmis, Andrew N W Hone, Thomas Stibor, Edward B Clark

    Abstract:

    Artificial Immune Systems (AIS) constitute a relatively new area of bio-inspired computing. Biological models of the natural Immune system, in particular the theories of clonal selection, Immune networks and negative selection, have provided the inspiration for AIS algorithms. Moreover, such algorithms have been successfully employed in a wide variety of different application areas. However, despite these practical successes, until recently there has been a dearth of theory to justify their use. In this paper, the existing theoretical work on AIS is reviewed. After the presentation of a simple example of each of the three main types of AIS algorithm (that is, clonal selection, Immune network and negative selection algorithms respectively), details of the theoretical analysis for each of these types are given. Some of the future challenges in this area are also highlighted.

  • revisiting the foundations of Artificial Immune Systems for data mining
    IEEE Transactions on Evolutionary Computation, 2007
    Co-Authors: Alex A Freitas, Jon Timmis

    Abstract:

    This paper advocates a problem-oriented approach for the design of Artificial Immune Systems (AIS) for data mining. By problem-oriented approach we mean that, in real-world data mining applications the design of an AIS should take into account the characteristics of the data to be mined together with the application domain: the components of the AIS – such as its representation, affinity function, and Immune process – should be tailored for the data and the application. This is in contrast with the majority of the literature, where a very generic AIS algorithm for data mining is developed and there is little or no concern in tailoring the components of the AIS for the data to be mined or the application domain. To support this problem-oriented approach, we provide an extensive critical review of the current literature on AIS for data mining, focusing on the data mining tasks of classification and anomaly detection. We discuss several important lessons to be taken from the natural Immune system to design new AIS that are considerably more adaptive than current AIS. Finally, we conclude this paper with a summary of seven limitations of current AIS for data mining and ten suggested research directions.

Melanie E Moses – One of the best experts on this subject based on the ideXlab platform.

  • scale invariance of Immune system response rates and times perspectives on Immune system architecture and implications for Artificial Immune Systems
    Swarm Intelligence, 2010
    Co-Authors: Soumya Banerjee, Melanie E Moses

    Abstract:

    Most biological rates and times decrease systematically with increasing organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural Immune system (NIS) response rates do not change systematically with body size. The scale-invariant detection and response of the NIS is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate three different hypothesized NIS architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response. This leads to nearly scale-invariant detection and response consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on distributed Systems, for example low-powered robots connected by short-range wireless communication. We show that the sub-modular design principles of the NIS can be applied to problems such as distributed robot control to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution. We demonstrate that the lymphatic network of the NIS efficiently balances local and global communication, and we suggest a new approach for Artificial Immune Systems (AIS) that uses a sub-modular architecture to facilitate distributed search.

  • scale invariance of Immune system response rates and times perspectives on Immune system architecture and implications for Artificial Immune Systems
    arXiv: Quantitative Methods, 2010
    Co-Authors: Soumya Banerjee, Melanie E Moses

    Abstract:

    Most biological rates and times decrease systematically with organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural Immune system (NIS) response rates do not change systematically with body size. This is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We call this scale-invariant detection and response. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate a range of architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response using antibodies. This leads to nearly scale-invariant detection and response, consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on Artificial Immune Systems (AIS), especially distributed Systems applications used to connect low-powered sensors using short-range wireless communication. We show that AIS problems, like distributed robot control, will also require a sub-modular architecture to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution between different components. This research has wide applicability in other distributed Systems AIS applications.

Soumya Banerjee – One of the best experts on this subject based on the ideXlab platform.

  • scale invariance of Immune system response rates and times perspectives on Immune system architecture and implications for Artificial Immune Systems
    Swarm Intelligence, 2010
    Co-Authors: Soumya Banerjee, Melanie E Moses

    Abstract:

    Most biological rates and times decrease systematically with increasing organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural Immune system (NIS) response rates do not change systematically with body size. The scale-invariant detection and response of the NIS is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate three different hypothesized NIS architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response. This leads to nearly scale-invariant detection and response consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on distributed Systems, for example low-powered robots connected by short-range wireless communication. We show that the sub-modular design principles of the NIS can be applied to problems such as distributed robot control to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution. We demonstrate that the lymphatic network of the NIS efficiently balances local and global communication, and we suggest a new approach for Artificial Immune Systems (AIS) that uses a sub-modular architecture to facilitate distributed search.

  • scale invariance of Immune system response rates and times perspectives on Immune system architecture and implications for Artificial Immune Systems
    arXiv: Quantitative Methods, 2010
    Co-Authors: Soumya Banerjee, Melanie E Moses

    Abstract:

    Most biological rates and times decrease systematically with organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural Immune system (NIS) response rates do not change systematically with body size. This is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We call this scale-invariant detection and response. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate a range of architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response using antibodies. This leads to nearly scale-invariant detection and response, consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on Artificial Immune Systems (AIS), especially distributed Systems applications used to connect low-powered sensors using short-range wireless communication. We show that AIS problems, like distributed robot control, will also require a sub-modular architecture to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution between different components. This research has wide applicability in other distributed Systems AIS applications.